Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 74287

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 81446

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-12-02T21:14:34+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	155438
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	153404	0
dateAnnounced	0	1.00	10	10	0	309	0
gender	49120	0.68	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	151132	0.03	8	23	0	8	0
notes	84255	0.46	1	270	0	68232	1
mhlwPatientNumber	154989	0.00	1	11	0	434	0
prefecturePatientNumber	44785	0.71	5	20	0	110644	0
prefectureSourceURL	124046	0.20	5	224	0	3454	0
residence	57297	0.63	1	38	0	1429	0
sourceURL	1205	0.99	1	239	0	9103	0
relatedPatients	143494	0.08	2	259	0	7126	0
knownCluster	152904	0.02	3	88	0	235	0
detectedCityTown	127197	0.18	2	22	0	667	0
cityPrefectureNumber	127498	0.18	1	34	0	27931	2
citySourceURL	143223	0.08	9	317	0	3692	0
deceasedDate	153291	0.01	10	10	0	259	0
deceasedReportedDate	154214	0.01	10	62	0	208	0
deathSourceURL	154359	0.01	14	123	0	658	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 153403, FAL: 2035
charterFlightPassenger	155424	0	1.00	TRU: 14
cruisePassengerDisembarked	155427	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	25.65	24.94	-1	-1	20	40	100	▇▆▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	151839
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	151839	0
knownCluster	149355	0.02	3	88	0	232	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-12-02	2020-09-16	306
deceasedDate	151460	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	151510	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	47118	0.69	FALSE	2	M: 58605, F: 46116
patientStatus	149328	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 47203, 20: 28223, 30: 18168, 40: 15261
pcode	0	1.00	FALSE	47	13: 41860, 27: 21019, 14: 12954, 23: 10540
pref	0	1.00	FALSE	47	東京都: 41860, 大阪府: 21019, 神奈川: 12954, 愛知県: 10540
region	0	1.00	FALSE	8	関東地: 74287, 近畿地: 32937, 中部地: 16517, 九州地: 13431
広域圏	13646	0.91	FALSE	8	首都圏: 74649, 近畿圏: 32058, 中部圏: 15053, 九州圏: 9025
通俗的区分	0	1.00	FALSE	11	関東: 74287, 関西: 32058, 東海: 14306, 北海道: 9240
fct_pref	0	1.00	FALSE	47	Tok: 41860, Osa: 21019, Kan: 12954, Aic: 10540

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 151839
charterFlightPassenger	151832	0	1.00	TRU: 7
cruisePassengerDisembarked	151828	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 149355, TRU: 2484

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.2）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.2）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

厚生労働省のデータと乖離がある。 　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
                       linetype = "dashed", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・同移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計死亡者数（実線）")
    )

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 228.5118 196.6340 204.4298 181.4788 167.0333 189.9989 179.2852 196.5486
##  [9] 182.0874 186.3500 180.4353 177.3358 183.3693 182.0543
## 
## $北海道地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 214.6569 207.3225
## 47.28571 180.7469 172.3367
## 47.42857 185.0360 174.7695
## 47.57143 161.4553 150.8555
## 47.71429 145.8530 134.6409
## 47.85714 167.0738 154.9381
## 48.00000 154.7391 141.7452
## 48.14286 167.8322 152.6306
## 48.28571 150.8818 134.3626
## 48.42857 152.2717 134.2317
## 48.57143 144.6326 125.6798
## 48.71429 139.6301 119.6699
## 48.85714 143.5967 122.5424
## 49.00000 140.3171 118.2227
## 
## $北海道地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 242.3668 249.7012
## 47.28571 212.5211 220.9313
## 47.42857 223.8236 234.0901
## 47.57143 201.5023 212.1021
## 47.71429 188.2135 199.4256
## 47.85714 212.9239 225.0596
## 48.00000 203.8313 216.8252
## 48.14286 225.2651 240.4666
## 48.28571 213.2930 229.8122
## 48.42857 220.4284 238.4684
## 48.57143 216.2379 235.1907
## 48.71429 215.0416 235.0018
## 48.85714 223.1419 244.1963
## 49.00000 223.7915 245.8859
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 33.37326 33.88994 33.91910 31.38924 29.88802 28.58997 29.45118 30.70338
##  [9] 32.50111 33.28958 33.35468 32.41802 31.47935 30.82119
## 
## $東北地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 26.57837 22.98136
## 47.28571 26.76178 22.98836
## 47.42857 26.72127 22.91097
## 47.57143 24.19110 20.38063
## 47.71429 22.54570 18.65892
## 47.85714 21.23387 17.33978
## 48.00000 21.88777 17.88394
## 48.14286 22.87821 18.73581
## 48.28571 24.19741 19.80170
## 48.42857 24.73702 20.20957
## 48.57143 24.61155 19.98322
## 48.71429 23.60149 18.93430
## 48.85714 22.59407 17.89048
## 49.00000 21.87301 17.13612
## 
## $東北地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 40.16816 43.76516
## 47.28571 41.01810 44.79152
## 47.42857 41.11694 44.92724
## 47.57143 38.58738 42.39785
## 47.71429 37.23033 41.11711
## 47.85714 35.94607 39.84015
## 48.00000 37.01459 41.01841
## 48.14286 38.52855 42.67095
## 48.28571 40.80481 45.20052
## 48.42857 41.84215 46.36960
## 48.57143 42.09781 46.72614
## 48.71429 41.23454 45.90173
## 48.85714 40.36463 45.06821
## 49.00000 39.76938 44.50626
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 1172.7961 1154.4995 1189.6308  978.2352  812.0422  950.0732 1142.1906
##  [8] 1271.0953 1261.3610 1301.1938 1089.6517  920.1655 1054.4151 1244.2653
## 
## $関東地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 1096.9512 1056.8013
## 47.28571 1057.4214 1006.0313
## 47.42857 1084.4670 1028.7967
## 47.57143  869.7046  812.2520
## 47.71429  700.8533  641.9935
## 47.85714  834.8878  773.9124
## 48.00000 1020.6455  956.3034
## 48.14286 1133.9592 1061.3638
## 48.28571 1111.0644 1031.5022
## 48.42857 1141.3553 1056.7419
## 48.57143  922.9441  834.6944
## 48.71429  747.8029  656.5597
## 48.85714  876.4625  782.2601
## 49.00000 1060.1670  962.7113
## 
## $関東地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%      95%
## 47.14286 1248.6411 1288.791
## 47.28571 1251.5776 1302.968
## 47.42857 1294.7945 1350.465
## 47.57143 1086.7658 1144.218
## 47.71429  923.2311  982.091
## 47.85714 1065.2585 1126.234
## 48.00000 1263.7357 1328.078
## 48.14286 1408.2313 1480.827
## 48.28571 1411.6575 1491.220
## 48.42857 1461.0323 1545.646
## 48.57143 1256.3594 1344.609
## 48.71429 1092.5280 1183.771
## 48.85714 1232.3677 1326.570
## 49.00000 1428.3636 1525.819
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 370.5864 361.8793 384.7152 299.2069 277.1204 315.1167 372.2724 379.7010
##  [9] 398.7390 392.8505 346.6376 304.3834 359.4681 383.9975
## 
## $中部地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 346.3015 333.4459
## 47.28571 331.4958 315.4118
## 47.42857 349.4689 330.8107
## 47.57143 261.0683 240.8790
## 47.71429 235.9020 214.0824
## 47.85714 271.7966 248.8643
## 48.00000 326.4807 302.2400
## 48.14286 327.8210 300.3573
## 48.28571 341.9256 311.8504
## 48.42857 332.1948 300.0857
## 48.57143 282.3542 248.3247
## 48.71429 237.0077 201.3412
## 48.85714 289.0024 251.7001
## 49.00000 310.7971 272.0472
## 
## $中部地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 394.8713 407.7270
## 47.28571 392.2627 408.3467
## 47.42857 419.9615 438.6198
## 47.57143 337.3456 357.5349
## 47.71429 318.3387 340.1584
## 47.85714 358.4367 381.3690
## 48.00000 418.0640 442.3047
## 48.14286 431.5810 459.0446
## 48.28571 455.5524 485.6276
## 48.42857 453.5061 485.6153
## 48.57143 410.9210 444.9505
## 48.71429 371.7590 407.4255
## 48.85714 429.9338 467.2361
## 49.00000 457.1978 495.9478
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 679.4554 665.9069 739.8955 694.6271 513.8895 540.5397 650.2941 711.2366
##  [9] 697.4001 769.2864 705.4880 566.1818 600.6235 695.6821
## 
## $近畿地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 634.6919 610.9955
## 47.28571 612.1155 583.6400
## 47.42857 683.1657 653.1348
## 47.57143 635.1038 603.5941
## 47.71429 451.6981 418.7759
## 47.85714 475.7900 441.5135
## 48.00000 583.0833 547.5041
## 48.14286 634.6964 594.1785
## 48.28571 615.0380 571.4382
## 48.42857 683.0645 637.4214
## 48.57143 615.5718 567.9730
## 48.71429 472.7171 423.2399
## 48.85714 503.7403 452.4534
## 49.00000 595.4969 542.4620
## 
## $近畿地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 724.2188 747.9152
## 47.28571 719.6983 748.1737
## 47.42857 796.6253 826.6562
## 47.57143 754.1504 785.6602
## 47.71429 576.0810 609.0031
## 47.85714 605.2895 639.5659
## 48.00000 717.5048 753.0840
## 48.14286 787.7769 828.2948
## 48.28571 779.7622 823.3620
## 48.42857 855.5084 901.1515
## 48.57143 795.4043 843.0031
## 48.71429 659.6465 709.1237
## 48.85714 697.5068 748.7936
## 49.00000 795.8673 848.9021
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 36.27016 35.10159 35.10159 35.10159 35.10159 35.10159 35.10159 35.10159
##  [9] 35.10159 35.10159 35.10159 35.10159 35.10159 35.10159
## 
## $中国地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 26.68914 21.61726
## 47.28571 24.86449 19.44529
## 47.42857 24.60776 19.05266
## 47.57143 24.35716 18.66941
## 47.71429 24.11228 18.29489
## 47.85714 23.87274 17.92854
## 48.00000 23.63820 17.56984
## 48.14286 23.40836 17.21834
## 48.28571 23.18296 16.87361
## 48.42857 22.96174 16.53528
## 48.57143 22.74448 16.20301
## 48.71429 22.53097 15.87648
## 48.85714 22.32103 15.55541
## 49.00000 22.11448 15.23952
## 
## $中国地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 45.85117 50.92305
## 47.28571 45.33870 50.75790
## 47.42857 45.59543 51.15053
## 47.57143 45.84602 51.53378
## 47.71429 46.09091 51.90830
## 47.85714 46.33045 52.27465
## 48.00000 46.56499 52.63335
## 48.14286 46.79483 52.98485
## 48.28571 47.02023 53.32958
## 48.42857 47.24145 53.66790
## 48.57143 47.45871 54.00018
## 48.71429 47.67222 54.32671
## 48.85714 47.88216 54.64778
## 49.00000 48.08871 54.96367
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 12.94284 12.47957 11.50759 11.71194 11.03182 12.40011 12.24357 11.81346
##  [9] 12.29013 12.91551 13.21457 13.15696 12.79637 12.41356
## 
## $四国地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 9.143714 7.132579
## 47.28571 8.190835 5.920517
## 47.42857 6.984278 4.589779
## 47.57143 6.965623 4.453074
## 47.71429 6.072519 3.447221
## 47.85714 7.236598 4.503200
## 48.00000 6.883624 4.046241
## 48.14286 6.321541 3.414297
## 48.28571 6.643786 3.654794
## 48.42857 7.110823 4.038010
## 48.57143 7.255752 4.101345
## 48.71429 7.047893 3.813950
## 48.85714 6.540666 3.229097
## 49.00000 6.014578 2.627161
## 
## $四国地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 16.74196 18.75309
## 47.28571 16.76830 19.03862
## 47.42857 16.03091 18.42541
## 47.57143 16.45826 18.97081
## 47.71429 15.99113 18.61642
## 47.85714 17.56362 20.29702
## 48.00000 17.60351 20.44090
## 48.14286 17.30537 20.21262
## 48.28571 17.93647 20.92546
## 48.42857 18.72019 21.79300
## 48.57143 19.17339 22.32779
## 48.71429 19.26602 22.49996
## 48.85714 19.05207 22.36364
## 49.00000 18.81255 22.19996
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 178.5420 163.3475 179.8983 156.1541 138.3373 142.9850 158.8936 189.7108
##  [9] 178.0344 189.0938 171.6463 160.7623 168.0587 175.1942
## 
## $九州地方$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 155.88701 143.89419
## 47.28571 135.57782 120.87742
## 47.42857 148.27389 131.53295
## 47.57143 123.34088 105.97059
## 47.71429 103.44499  84.97411
## 47.85714 103.98285  83.33634
## 48.00000 116.59111  94.19754
## 48.14286 140.88363 115.03610
## 48.28571 124.20223  95.70521
## 48.42857 130.03175  98.76618
## 48.57143 109.27315  76.25480
## 48.71429  94.75318  59.81007
## 48.85714  98.09073  61.05191
## 49.00000 101.51602  62.51314
## 
## $九州地方$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 201.1970 213.1898
## 47.28571 191.1172 205.8176
## 47.42857 211.5227 228.2636
## 47.57143 188.9674 206.3377
## 47.71429 173.2296 191.7005
## 47.85714 181.9872 202.6337
## 48.00000 201.1960 223.5896
## 48.14286 238.5379 264.3854
## 48.28571 231.8665 260.3635
## 48.42857 248.1559 279.4214
## 48.57143 234.0194 267.0378
## 48.71429 226.7714 261.7145
## 48.85714 238.0267 275.0656
## 49.00000 248.8724 287.8753

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 228.5118 196.6340 204.4298 181.4788 167.0333 189.9989 179.2852 196.5486
##  [9] 182.0874 186.3500 180.4353 177.3358 183.3693 182.0543
## 
## $北海道$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 214.6569 207.3225
## 47.28571 180.7469 172.3367
## 47.42857 185.0360 174.7695
## 47.57143 161.4553 150.8555
## 47.71429 145.8530 134.6409
## 47.85714 167.0738 154.9381
## 48.00000 154.7391 141.7452
## 48.14286 167.8322 152.6306
## 48.28571 150.8818 134.3626
## 48.42857 152.2717 134.2317
## 48.57143 144.6326 125.6798
## 48.71429 139.6301 119.6699
## 48.85714 143.5967 122.5424
## 49.00000 140.3171 118.2227
## 
## $北海道$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 242.3668 249.7012
## 47.28571 212.5211 220.9313
## 47.42857 223.8236 234.0901
## 47.57143 201.5023 212.1021
## 47.71429 188.2135 199.4256
## 47.85714 212.9239 225.0596
## 48.00000 203.8313 216.8252
## 48.14286 225.2651 240.4666
## 48.28571 213.2930 229.8122
## 48.42857 220.4284 238.4684
## 48.57143 216.2379 235.1907
## 48.71429 215.0416 235.0018
## 48.85714 223.1419 244.1963
## 49.00000 223.7915 245.8859
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 6.986522 4.580672 6.065576 5.149085 5.714748 5.365618 5.581103 5.448104
##  [9] 5.530192 5.479527 5.510798 5.491497 5.503409 5.496057
## 
## $青森県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%         95%
## 47.14286 4.4034598  3.03606932
## 47.28571 1.5652894 -0.03095763
## 47.42857 2.9604052  1.31662702
## 47.57143 1.8040016  0.03322154
## 47.71429 2.2417892  0.40331566
## 47.85714 1.7149228 -0.21763818
## 48.00000 1.7940601 -0.21067932
## 48.14286 1.5100774 -0.57458804
## 48.28571 1.4584359 -0.69702143
## 48.42857 1.2713647 -0.95630170
## 48.57143 1.1746817 -1.12071935
## 48.71429 1.0286033 -1.33391004
## 48.85714 0.9187411 -1.50823573
## 49.00000 0.7918645 -1.69838468
## 
## $青森県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286  9.569584 10.936974
## 47.28571  7.596054  9.192301
## 47.42857  9.170747 10.814525
## 47.57143  8.494168 10.264948
## 47.71429  9.187707 11.026181
## 47.85714  9.016312 10.948873
## 48.00000  9.368146 11.372885
## 48.14286  9.386131 11.470796
## 48.28571  9.601948 11.757405
## 48.42857  9.687689 11.915355
## 48.57143  9.846913 12.142314
## 48.71429  9.954391 12.316904
## 48.85714 10.088078 12.515055
## 49.00000 10.200250 12.690499
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 10.270644  4.830166  7.799503  7.163248  5.899479  9.039849  4.502386
##  [8]  9.536909  4.738352  8.731290  6.068281  7.092242  7.711022  5.662583
## 
## $岩手県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 8.664414 7.8141272
## 47.28571 2.994022 2.0220263
## 47.42857 5.719737 4.6187755
## 47.57143 5.022020 3.8885222
## 47.71429 3.600981 2.3842302
## 47.85714 6.515841 5.1797111
## 48.00000 1.887368 0.5030616
## 48.14286 6.719290 5.2277316
## 48.28571 1.867694 0.3480591
## 48.42857 5.659181 4.0329037
## 48.57143 2.924204 1.2598302
## 48.71429 3.806692 2.0674269
## 48.85714 4.328177 2.5374079
## 49.00000 2.190579 0.3526110
## 
## $岩手県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 11.876873 12.727160
## 47.28571  6.666310  7.638306
## 47.42857  9.879268 10.980230
## 47.57143  9.304476 10.437973
## 47.71429  8.197977  9.414728
## 47.85714 11.563858 12.899988
## 48.00000  7.117404  8.501711
## 48.14286 12.354529 13.846087
## 48.28571  7.609010  9.128645
## 48.42857 11.803400 13.429677
## 48.57143  9.212358 10.876732
## 48.71429 10.377792 12.117057
## 48.85714 11.093866 12.884635
## 49.00000  9.134586 10.972554
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 11.818958 10.491498 10.054659  8.903885  9.399782  9.909588 11.208303
##  [8] 11.985790 12.633834 12.561116 12.281092 11.712460 11.308611 11.036674
## 
## $宮城県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 6.776002 4.1064232
## 47.28571 5.388807 2.6876050
## 47.42857 4.751241 1.9437812
## 47.57143 3.591039 0.7785874
## 47.71429 3.857916 0.9242293
## 47.85714 4.221273 1.2100610
## 48.00000 5.191075 2.0057474
## 48.14286 5.788873 2.5084230
## 48.28571 6.254927 2.8781376
## 48.42857 6.109554 2.6943029
## 48.57143 5.767993 2.3201673
## 48.71429 5.166921 1.7019213
## 48.85714 4.715943 1.2259955
## 49.00000 4.391089 0.8731296
## 
## $宮城県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 16.86191 19.53149
## 47.28571 15.59419 18.29539
## 47.42857 15.35808 18.16554
## 47.57143 14.21673 17.02918
## 47.71429 14.94165 17.87534
## 47.85714 15.59790 18.60911
## 48.00000 17.22553 20.41086
## 48.14286 18.18271 21.46316
## 48.28571 19.01274 22.38953
## 48.42857 19.01268 22.42793
## 48.57143 18.79419 22.24202
## 48.71429 18.25800 21.72300
## 48.85714 17.90128 21.39123
## 49.00000 17.68226 21.20022
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 0.4554768 0.5101998 0.5639984 0.5761356 0.5831717 0.5852754 0.5862577
##  [8] 0.5865938 0.5867360 0.5867880 0.5868090 0.5868169 0.5868200 0.5868212
## 
## $秋田県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                 80%       95%
## 47.14286 -0.9012689 -1.619487
## 47.28571 -0.8598841 -1.585163
## 47.42857 -0.8193745 -1.551688
## 47.57143 -0.8090305 -1.542293
## 47.71429 -0.8031261 -1.536988
## 47.85714 -0.8016506 -1.535845
## 48.00000 -0.8012034 -1.535681
## 48.14286 -0.8013516 -1.536085
## 48.28571 -0.8016789 -1.536661
## 48.42857 -0.8020896 -1.537317
## 48.57143 -0.8025288 -1.538000
## 48.71429 -0.8029799 -1.538694
## 48.85714 -0.8034354 -1.539392
## 49.00000 -0.8038924 -1.540092
## 
## $秋田県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 1.812223 2.530440
## 47.28571 1.880284 2.605562
## 47.42857 1.947371 2.679685
## 47.57143 1.961302 2.694564
## 47.71429 1.969470 2.703331
## 47.85714 1.972201 2.706396
## 48.00000 1.973719 2.708196
## 48.14286 1.974539 2.709273
## 48.28571 1.975151 2.710133
## 48.42857 1.975666 2.710893
## 48.57143 1.976147 2.711618
## 48.71429 1.976614 2.712328
## 48.85714 1.977075 2.713032
## 49.00000 1.977535 2.713734
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 3.552520 3.381759 3.220788 3.069048 2.926008 2.791170 2.664063 2.544245
##  [9] 2.431297 2.324825 2.224458 2.129846 2.040659 1.956586
## 
## $山形県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%          95%
## 47.14286 2.2963776  1.631415710
## 47.28571 2.0597165  1.359869623
## 47.42857 1.8428293  1.113381858
## 47.57143 1.6432395  0.888462209
## 47.71429 1.4589894  0.682396639
## 47.85714 1.2884795  0.493003210
## 48.00000 1.1303706  0.318482721
## 48.14286 0.9835199  0.157322037
## 48.28571 0.8469376  0.008228587
## 48.42857 0.7197563 -0.129915718
## 48.57143 0.6012085 -0.258087967
## 48.71429 0.4906100 -0.377149251
## 48.85714 0.3873470 -0.487863777
## 49.00000 0.2908656 -0.590913791
## 
## $山形県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 4.808663 5.473625
## 47.28571 4.703801 5.403648
## 47.42857 4.598747 5.328195
## 47.57143 4.494856 5.249633
## 47.71429 4.393027 5.169619
## 47.85714 4.293860 5.089337
## 48.00000 4.197756 5.009644
## 48.14286 4.104970 4.931168
## 48.28571 4.015656 4.854365
## 48.42857 3.929894 4.779566
## 48.57143 3.847708 4.707004
## 48.71429 3.769083 4.636842
## 48.85714 3.693972 4.569183
## 49.00000 3.622307 4.504087
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 3.274916 3.274916 3.274916 3.274916 3.274916 3.274916 3.274916 3.274916
##  [9] 3.274916 3.274916 3.274916 3.274916 3.274916 3.274916
## 
## $福島県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                  80%        95%
## 47.14286  0.62239071 -0.7817716
## 47.28571  0.55813042 -0.8800492
## 47.42857  0.49535535 -0.9760554
## 47.57143  0.43396706 -1.0699407
## 47.71429  0.37387750 -1.1618397
## 47.85714  0.31500758 -1.2518735
## 48.00000  0.25728592 -1.3401512
## 48.14286  0.20064783 -1.4267716
## 48.28571  0.14503449 -1.5118249
## 48.42857  0.09039221 -1.5953931
## 48.57143  0.03667184 -1.6775513
## 48.71429 -0.01617178 -1.7583686
## 48.85714 -0.06818021 -1.8379086
## 49.00000 -0.11939184 -1.9162301
## 
## $福島県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 5.927442 7.331604
## 47.28571 5.991702 7.429882
## 47.42857 6.054477 7.525888
## 47.57143 6.115866 7.619773
## 47.71429 6.175955 7.711672
## 47.85714 6.234825 7.801706
## 48.00000 6.292547 7.889984
## 48.14286 6.349185 7.976604
## 48.28571 6.404798 8.061658
## 48.42857 6.459441 8.145226
## 48.57143 6.513161 8.227384
## 48.71429 6.566005 8.308201
## 48.85714 6.618013 8.387741
## 49.00000 6.669225 8.466063
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 37.76797 40.37842 48.74815 40.56296 36.47202 34.50951 37.32265 40.76626
##  [9] 38.80102 43.08881 34.75167 33.68302 38.06241 37.56628
## 
## $茨城県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 32.14491 29.16824
## 47.28571 34.45197 31.31469
## 47.42857 42.80681 39.66165
## 47.57143 34.42082 31.16936
## 47.71429 29.94869 26.49545
## 47.85714 27.50296 23.79392
## 48.00000 29.79238 25.80609
## 48.14286 32.48990 28.10865
## 48.28571 29.95478 25.27186
## 48.42857 33.74292 28.79550
## 48.57143 24.88965 19.66901
## 48.71429 23.30745 17.81496
## 48.85714 27.18492 21.42672
## 49.00000 26.20215 20.18635
## 
## $茨城県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 43.39103 46.36769
## 47.28571 46.30487 49.44214
## 47.42857 54.68949 57.83465
## 47.57143 46.70510 49.95655
## 47.71429 42.99534 46.44858
## 47.85714 41.51605 45.22510
## 48.00000 44.85292 48.83920
## 48.14286 49.04262 53.42387
## 48.28571 47.64727 52.33019
## 48.42857 52.43469 57.38211
## 48.57143 44.61368 49.83432
## 48.71429 44.05858 49.55108
## 48.85714 48.93991 54.69811
## 49.00000 48.93040 54.94621
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 13.92430 15.24841 16.21428 16.57935 14.68396 19.35837 18.86770 17.02907
##  [9] 17.02907 17.02907 17.02907 17.02907 17.02907 17.02907
## 
## $栃木県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 10.40142  8.536524
## 47.28571 11.62144  9.701429
## 47.42857 12.48611 10.512536
## 47.57143 12.75267 10.726939
## 47.71429 10.76123  8.684656
## 47.85714 15.34189 13.215694
## 48.00000 14.75961 12.584917
## 48.14286 12.58668 10.235016
## 48.28571 12.46069 10.042333
## 48.42857 12.33808  9.854823
## 48.57143 12.21860  9.672091
## 48.71429 12.10202  9.493788
## 48.85714 11.98813  9.319609
## 49.00000 11.87675  9.149278
## 
## $栃木県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 17.44719 19.31209
## 47.28571 18.87539 20.79540
## 47.42857 19.94245 21.91603
## 47.57143 20.40604 22.43177
## 47.71429 18.60669 20.68326
## 47.85714 23.37485 25.50105
## 48.00000 22.97579 25.15048
## 48.14286 21.47147 23.82313
## 48.28571 21.59746 24.01581
## 48.42857 21.72006 24.20332
## 48.57143 21.83955 24.38606
## 48.71429 21.95613 24.56436
## 48.85714 22.07002 24.73854
## 49.00000 22.18139 24.90887
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 36.28214 29.24939 32.11592 30.79848 30.12521 32.49429 33.99102 32.84746
##  [9] 31.95498 32.33661 32.33479 32.33600 32.33520 32.33573
## 
## $群馬県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 31.26785 28.61344
## 47.28571 23.15974 19.93607
## 47.42857 25.70450 22.31050
## 47.57143 24.27472 20.82125
## 47.71429 23.11844 19.40928
## 47.85714 25.29317 21.48113
## 48.00000 26.44530 22.45083
## 48.14286 24.90105 20.69448
## 48.28571 23.60773 19.18897
## 48.42857 23.70394 19.13408
## 48.57143 23.42250 18.70461
## 48.71429 23.12942 18.25575
## 48.85714 22.85844 17.84174
## 49.00000 22.58666 17.42582
## 
## $群馬県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 41.29644 43.95084
## 47.28571 35.33903 38.56270
## 47.42857 38.52734 41.92134
## 47.57143 37.32224 40.77571
## 47.71429 37.13198 40.84113
## 47.85714 39.69542 43.50746
## 48.00000 41.53673 45.53120
## 48.14286 40.79386 45.00043
## 48.28571 40.30223 44.72099
## 48.42857 40.96928 45.53914
## 48.57143 41.24709 45.96498
## 48.71429 41.54258 46.41624
## 48.85714 41.81196 46.82866
## 49.00000 42.08479 47.24564
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 133.1067 112.5855 125.9469 131.0752 105.0156 115.1962 131.5307 130.0136
##  [9] 121.5113 121.2456 127.8973 117.1335 126.1878 130.9394
## 
## $埼玉県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 116.38710 107.53628
## 47.28571  95.13020  85.88990
## 47.42857 108.43956  99.17172
## 47.57143 112.98365 103.40657
## 47.71429  86.35816  76.48150
## 47.85714  95.98949  85.82208
## 48.00000 111.79000 101.33992
## 48.14286 107.84169  96.10459
## 48.28571  98.48345  86.29324
## 48.42857  97.67971  85.20469
## 48.57143 103.56232  90.68015
## 48.71429  92.05292  78.77608
## 48.85714 100.38318  86.72306
## 49.00000 104.43052  90.39759
## 
## $埼玉県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 149.8263 158.6771
## 47.28571 130.0409 139.2812
## 47.42857 143.4543 152.7221
## 47.57143 149.1668 158.7439
## 47.71429 123.6731 133.5497
## 47.85714 134.4029 144.5703
## 48.00000 151.2713 161.7214
## 48.14286 152.1855 163.9226
## 48.28571 144.5392 156.7294
## 48.42857 144.8114 157.2864
## 48.57143 152.2323 165.1145
## 48.71429 142.2140 155.4909
## 48.85714 151.9924 165.6525
## 49.00000 157.4482 171.4812
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 83.51267 89.10316 90.86168 80.09645 74.74335 73.73484 78.89043 79.24619
##  [9] 84.79462 84.69722 79.38304 78.65033 80.47871 80.02567
## 
## $千葉県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 70.99449 64.36777
## 47.28571 75.09532 67.68002
## 47.42857 76.37685 68.70905
## 47.57143 65.14985 57.23760
## 47.71429 59.34883 51.19946
## 47.85714 57.90507 49.52529
## 48.00000 62.63705 54.03303
## 48.14286 61.67236 52.36933
## 48.28571 66.47003 56.76958
## 48.42857 65.81159 55.81414
## 48.57143 59.95256 49.66669
## 48.71429 58.68987 48.12344
## 48.85714 60.00198 49.16225
## 49.00000 59.04537 47.93906
## 
## $千葉県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286  96.03084 102.65756
## 47.28571 103.11100 110.52630
## 47.42857 105.34650 113.01431
## 47.57143  95.04304 102.95529
## 47.71429  90.13787  98.28724
## 47.85714  89.56462  97.94440
## 48.00000  95.14381 103.74783
## 48.14286  96.82003 106.12305
## 48.28571 103.11920 112.81965
## 48.42857 103.58285 113.58029
## 48.57143  98.81352 109.09939
## 48.71429  98.61079 109.17722
## 48.85714 100.95544 111.79517
## 49.00000 101.00597 112.11227
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 550.3282 556.8137 542.2004 407.2649 328.5440 382.2574 466.6383 528.0205
##  [9] 524.5311 523.2346 396.9477 321.7348 369.3171 448.7941
## 
## $東京都$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 501.0761 475.0036
## 47.28571 500.8057 471.1569
## 47.42857 482.0864 450.2639
## 47.57143 344.9710 311.9946
## 47.71429 265.8114 232.6027
## 47.85714 318.7463 285.1255
## 48.00000 401.9983 367.7799
## 48.14286 456.1801 418.1501
## 48.28571 448.5619 408.3463
## 48.42857 443.5390 401.3508
## 48.57143 314.0149 270.1129
## 48.71429 236.3470 191.1454
## 48.85714 281.2491 234.6287
## 49.00000 357.9095 309.7981
## 
## $東京都$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 599.5803 625.6528
## 47.28571 612.8217 642.4706
## 47.42857 602.3144 634.1369
## 47.57143 469.5589 502.5353
## 47.71429 391.2766 424.4852
## 47.85714 445.7684 479.3892
## 48.00000 531.2783 565.4967
## 48.14286 599.8608 637.8908
## 48.28571 600.5003 640.7159
## 48.42857 602.9301 645.1184
## 48.57143 479.8806 523.7826
## 48.71429 407.1226 452.3241
## 48.85714 457.3852 504.0056
## 49.00000 539.6787 587.7901
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 244.2084 226.8014 221.7705 181.1390 126.6664 180.7189 226.3935 248.8157
##  [9] 240.3317 235.8481 201.9373 148.7930 194.9296 240.0289
## 
## $神奈川県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 222.56830 211.11272
## 47.28571 202.36953 189.43606
## 47.42857 196.18887 182.64676
## 47.57143 154.81395 140.87833
## 47.71429  99.72896  85.46914
## 47.85714 153.21996 138.66290
## 48.00000 198.35736 183.51591
## 48.14286 217.37795 200.73580
## 48.28571 207.33170 189.86256
## 48.42857 201.76989 183.72999
## 48.57143 166.94492 148.42107
## 48.71429 112.95422  93.98232
## 48.85714 158.27959 138.87825
## 49.00000 202.59079 182.77224
## 
## $神奈川県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 265.8485 277.3041
## 47.28571 251.2333 264.1668
## 47.42857 247.3522 260.8943
## 47.57143 207.4640 221.3996
## 47.71429 153.6038 167.8637
## 47.85714 208.2179 222.7749
## 48.00000 254.4297 269.2711
## 48.14286 280.2535 296.8956
## 48.28571 273.3317 290.8008
## 48.42857 269.9263 287.9662
## 48.57143 236.9297 255.4536
## 48.71429 184.6318 203.6037
## 48.85714 231.5796 250.9810
## 49.00000 277.4670 297.2856
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 4.415514 3.416576 4.565727 3.644071 4.405486 3.728049 4.330770 3.794524
##  [9] 4.271626 3.847145 4.224809 3.888799 4.187749 3.921771
## 
## $新潟県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%        95%
## 47.14286 1.4011845 -0.1945053
## 47.28571 0.2679969 -1.3987600
## 47.42857 1.4145992 -0.2535073
## 47.57143 0.4230664 -1.2820302
## 47.71429 1.0986688 -0.6518544
## 47.85714 0.3990524 -1.3632120
## 48.00000 0.9272744 -0.8744271
## 48.14286 0.3652814 -1.4500501
## 48.28571 0.7763188 -1.0739850
## 48.42857 0.3232732 -1.5421519
## 48.57143 0.6412815 -1.2557233
## 48.71429 0.2745543 -1.6387110
## 48.85714 0.5188034 -1.4234193
## 49.00000 0.2205179 -1.7388068
## 
## $新潟県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 7.429844 9.025534
## 47.28571 6.565155 8.231912
## 47.42857 7.716855 9.384962
## 47.57143 6.865075 8.570171
## 47.71429 7.712303 9.462826
## 47.85714 7.057045 8.819310
## 48.00000 7.734265 9.535966
## 48.14286 7.223767 9.039099
## 48.28571 7.766933 9.617237
## 48.42857 7.371017 9.236442
## 48.57143 7.808336 9.705341
## 48.71429 7.503043 9.416308
## 48.85714 7.856696 9.798918
## 49.00000 7.623023 9.582348
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 1.047640 1.062720 1.077036 1.090628 1.103531 1.115782 1.127413 1.138455
##  [9] 1.148938 1.158891 1.168340 1.177311 1.185828 1.193914
## 
## $富山県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 -1.384277 -2.671657
## 47.28571 -1.523034 -2.891849
## 47.42857 -1.639923 -3.078195
## 47.57143 -1.739386 -3.237504
## 47.71429 -1.824645 -3.374728
## 47.85714 -1.898134 -3.493605
## 48.00000 -1.961747 -3.597049
## 48.14286 -2.016988 -3.687378
## 48.28571 -2.065077 -3.766474
## 48.42857 -2.107019 -3.835887
## 48.57143 -2.143648 -3.896908
## 48.71429 -2.175666 -3.950626
## 48.85714 -2.203671 -3.997963
## 49.00000 -2.228169 -4.039710
## 
## $富山県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 3.479558 4.766938
## 47.28571 3.648473 5.017288
## 47.42857 3.793995 5.232266
## 47.57143 3.920641 5.418759
## 47.71429 4.031708 5.581790
## 47.85714 4.129699 5.725170
## 48.00000 4.216573 5.851875
## 48.14286 4.293898 5.964288
## 48.28571 4.362954 6.064351
## 48.42857 4.424801 6.153670
## 48.57143 4.480328 6.233589
## 48.71429 4.530289 6.305248
## 48.85714 4.575327 6.369620
## 49.00000 4.615997 6.427539
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 3.197909 3.197909 3.197909 3.197909 3.197909 3.197909 3.197909 3.197909
##  [9] 3.197909 3.197909 3.197909 3.197909 3.197909 3.197909
## 
## $石川県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                 80%       95%
## 47.14286 -0.4164451 -2.329768
## 47.28571 -0.6178141 -2.637736
## 47.42857 -0.8090762 -2.930246
## 47.57143 -0.9916158 -3.209416
## 47.71429 -1.1665275 -3.476920
## 47.85714 -1.3346943 -3.734109
## 48.00000 -1.4968413 -3.982092
## 48.14286 -1.6535720 -4.221791
## 48.28571 -1.8053955 -4.453985
## 48.42857 -1.9527456 -4.679337
## 48.57143 -2.0959960 -4.898420
## 48.71429 -2.2354710 -5.111728
## 48.85714 -2.3714541 -5.319697
## 49.00000 -2.5041953 -5.522707
## 
## $石川県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 6.812262  8.725586
## 47.28571 7.013631  9.033553
## 47.42857 7.204893  9.326063
## 47.57143 7.387433  9.605233
## 47.71429 7.562345  9.872738
## 47.85714 7.730512 10.129927
## 48.00000 7.892659 10.377909
## 48.14286 8.049389 10.617608
## 48.28571 8.201213 10.849802
## 48.42857 8.348563 11.075155
## 48.57143 8.491813 11.294237
## 48.71429 8.631288 11.507546
## 48.85714 8.767271 11.715514
## 49.00000 8.900013 11.918524
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 1.3676999 1.0046493 1.0001870 0.9963742 0.9931163 0.9903326 0.9879540
##  [8] 0.9859216 0.9841850 0.9827011 0.9814332 0.9803499 0.9794242 0.9786332
## 
## $福井県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 -0.646608 -1.712918
## 47.28571 -1.146926 -2.285902
## 47.42857 -1.359022 -2.607912
## 47.57143 -1.503560 -2.826945
## 47.71429 -1.604751 -2.979978
## 47.85714 -1.676765 -3.088641
## 48.00000 -1.728574 -3.166618
## 48.14286 -1.766136 -3.222987
## 48.28571 -1.793525 -3.263956
## 48.42857 -1.813589 -3.293855
## 48.57143 -1.828345 -3.315751
## 48.71429 -1.839235 -3.331833
## 48.85714 -1.847299 -3.343675
## 49.00000 -1.853290 -3.352420
## 
## $福井県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 3.382008 4.448318
## 47.28571 3.156225 4.295200
## 47.42857 3.359396 4.608286
## 47.57143 3.496308 4.819693
## 47.71429 3.590983 4.966211
## 47.85714 3.657430 5.069306
## 48.00000 3.704482 5.142526
## 48.14286 3.737979 5.194830
## 48.28571 3.761895 5.232325
## 48.42857 3.778991 5.259258
## 48.57143 3.791211 5.278618
## 48.71429 3.799934 5.292532
## 48.85714 3.806147 5.302524
## 49.00000 3.810556 5.309686
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 6.801943 5.877353 5.488434 5.324839 5.256025 5.227079 5.214903 5.209782
##  [9] 5.207627 5.206721 5.206340 5.206179 5.206112 5.206084
## 
## $山梨県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 4.848361 3.814197
## 47.28571 3.666555 2.496229
## 47.42857 3.184470 1.964825
## 47.57143 2.970171 1.723685
## 47.71429 2.864605 1.598664
## 47.85714 2.804245 1.521675
## 48.00000 2.762946 1.464958
## 48.14286 2.729811 1.416993
## 48.28571 2.700272 1.372958
## 48.42857 2.672408 1.330824
## 48.57143 2.645408 1.289732
## 48.71429 2.618925 1.249315
## 48.85714 2.592809 1.209410
## 49.00000 2.566994 1.169944
## 
## $山梨県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 8.755524 9.789688
## 47.28571 8.088151 9.258477
## 47.42857 7.792398 9.012043
## 47.57143 7.679508 8.925994
## 47.71429 7.647445 8.913387
## 47.85714 7.649913 8.932483
## 48.00000 7.666860 8.964848
## 48.14286 7.689752 9.002570
## 48.28571 7.714982 9.042296
## 48.42857 7.741034 9.082618
## 48.57143 7.767272 9.122947
## 48.71429 7.793434 9.163044
## 48.85714 7.819415 9.202814
## 49.00000 7.845173 9.242223
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 14.57144 14.57144 14.57144 14.57144 14.57144 14.57144 14.57144 14.57144
##  [9] 14.57144 14.57144 14.57144 14.57144 14.57144 14.57144
## 
## $長野県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%      95%
## 47.14286 11.133226 9.313148
## 47.28571 10.881750 8.928548
## 47.42857 10.646352 8.568539
## 47.57143 10.424295 8.228932
## 47.71429 10.213538 7.906607
## 47.85714 10.012514 7.599168
## 48.00000  9.819988 7.304723
## 48.14286  9.634964 7.021754
## 48.28571  9.456629 6.749014
## 48.42857  9.284306 6.485469
## 48.57143  9.117425 6.230246
## 48.71429  8.955500 5.982604
## 48.85714  8.798115 5.741905
## 49.00000  8.644909 5.507595
## 
## $長野県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 18.00964 19.82972
## 47.28571 18.26112 20.21432
## 47.42857 18.49652 20.57433
## 47.57143 18.71857 20.91394
## 47.71429 18.92933 21.23626
## 47.85714 19.13036 21.54370
## 48.00000 19.32288 21.83815
## 48.14286 19.50791 22.12112
## 48.28571 19.68624 22.39386
## 48.42857 19.85856 22.65740
## 48.57143 20.02545 22.91262
## 48.71429 20.18737 23.16027
## 48.85714 20.34475 23.40097
## 49.00000 20.49796 23.63527
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 23.76125 25.98693 22.22511 21.72446 20.48934 22.76464 24.28962 24.98312
##  [9] 24.09969 22.37528 21.19090 21.47566 22.93860 24.35412
## 
## $岐阜県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 20.03263 18.05882
## 47.28571 22.02953 19.93461
## 47.42857 17.75189 15.38392
## 47.57143 16.98294 14.47292
## 47.71429 15.52926 12.90356
## 47.85714 17.67036 14.97362
## 48.00000 19.06998 16.30688
## 48.14286 19.37034 16.39911
## 48.28571 18.21264 15.09623
## 48.42857 16.14654 12.84924
## 48.57143 14.69763 11.26030
## 48.71429 14.78695 11.24617
## 48.85714 16.10586 12.48883
## 49.00000 17.37605 13.68209
## 
## $岐阜県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 27.48988 29.46369
## 47.28571 29.94433 32.03925
## 47.42857 26.69832 29.06630
## 47.57143 26.46599 28.97601
## 47.71429 25.44941 28.07512
## 47.85714 27.85892 30.55566
## 48.00000 29.50925 32.27236
## 48.14286 30.59590 33.56712
## 48.28571 29.98674 33.10316
## 48.42857 28.60402 31.90132
## 48.57143 27.68418 31.12151
## 48.71429 28.16437 31.70515
## 48.85714 29.77133 33.38836
## 49.00000 31.33218 35.02614
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 62.95764 58.51232 64.67698 60.24987 53.61702 53.22665 55.85468 58.51939
##  [9] 57.06384 59.46682 58.34605 55.72319 56.36271 57.06483
## 
## $静岡県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 55.72384 51.89450
## 47.28571 50.28573 45.93084
## 47.42857 56.08726 51.54014
## 47.57143 51.31176 46.58022
## 47.71429 44.34361 39.43456
## 47.85714 43.62964 38.54929
## 48.00000 45.94463 40.69857
## 48.14286 47.59437 41.81102
## 48.28571 45.55365 39.46052
## 48.42857 47.52065 41.19673
## 48.57143 45.97926 39.43268
## 48.71429 42.94963 36.18772
## 48.85714 43.19494 36.22434
## 49.00000 43.51431 36.34110
## 
## $静岡県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 70.19144 74.02078
## 47.28571 66.73891 71.09381
## 47.42857 73.26670 77.81382
## 47.57143 69.18797 73.91952
## 47.71429 62.89043 67.79947
## 47.85714 62.82366 67.90401
## 48.00000 65.76473 71.01079
## 48.14286 69.44440 75.22775
## 48.28571 68.57403 74.66716
## 48.42857 71.41299 77.73691
## 48.57143 70.71283 77.25941
## 48.71429 68.49675 75.25866
## 48.85714 69.53048 76.50108
## 49.00000 70.61536 77.78857
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 250.4993 265.9774 259.5517 211.1699 175.1570 231.3425 254.0802 279.3531
##  [9] 292.9676 287.3488 246.4027 215.8590 263.4864 282.7553
## 
## $愛知県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 232.4232 222.8543
## 47.28571 244.4001 232.9778
## 47.42857 233.8447 220.2362
## 47.57143 182.2669 166.9666
## 47.71429 143.2666 126.3848
## 47.85714 196.7573 178.4490
## 48.00000 216.9839 197.3463
## 48.14286 235.5324 212.3350
## 48.28571 244.8498 219.3777
## 48.42857 234.8171 207.0085
## 48.57143 189.9547 160.0729
## 48.71429 155.6990 123.8522
## 48.85714 199.8472 166.1586
## 49.00000 215.8119 180.3742
## 
## $愛知県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 268.5754 278.1443
## 47.28571 287.5548 298.9771
## 47.42857 285.2587 298.8672
## 47.57143 240.0729 255.3732
## 47.71429 207.0475 223.9293
## 47.85714 265.9278 284.2361
## 48.00000 291.1766 310.8142
## 48.14286 323.1739 346.3712
## 48.28571 341.0854 366.5574
## 48.42857 339.8804 367.6890
## 48.57143 302.8508 332.7325
## 48.71429 276.0189 307.8657
## 48.85714 327.1257 360.8143
## 49.00000 349.6987 385.1364
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 16.21342 13.93405 16.58546 14.66642 14.00479 12.89380 14.04891 13.96530
##  [9] 13.96530 13.96530 13.96530 13.96530 13.96530 13.96530
## 
## $三重県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%      95%
## 47.14286 12.000125 9.769740
## 47.28571  9.367961 6.950822
## 47.42857 11.691956 9.101492
## 47.57143  9.466069 6.713170
## 47.71429  8.514715 5.608446
## 47.85714  7.128543 4.076602
## 48.00000  8.021020 4.830051
## 48.14286  7.400817 3.925789
## 48.28571  7.069869 3.419648
## 48.42857  6.754094 2.936712
## 48.57143  6.451578 2.474053
## 48.71429  6.160779 2.029315
## 48.85714  5.880433 1.600563
## 49.00000  5.609488 1.186187
## 
## $三重県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 20.42672 22.65710
## 47.28571 18.50013 20.91727
## 47.42857 21.47896 24.06943
## 47.57143 19.86677 22.61967
## 47.71429 19.49486 22.40113
## 47.85714 18.65905 21.71099
## 48.00000 20.07679 23.26776
## 48.14286 20.52978 24.00481
## 48.28571 20.86073 24.51095
## 48.42857 21.17651 24.99389
## 48.57143 21.47902 25.45655
## 48.71429 21.76982 25.90129
## 48.85714 22.05017 26.33004
## 49.00000 22.32111 26.74441
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 7.574339 5.813941 6.878701 6.362899 6.680455 6.529566 6.624402 6.580347
##  [9] 6.608713 6.595879 6.604379 6.600651 6.603202 6.602123
## 
## $滋賀県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%        95%
## 47.14286 3.636837  1.5524493
## 47.28571 1.808048 -0.3125445
## 47.42857 2.464097  0.1271475
## 47.57143 1.861084 -0.5220336
## 47.71429 2.022347 -0.4435056
## 47.85714 1.785424 -0.7259728
## 48.00000 1.778687 -0.7864803
## 48.14286 1.652509 -0.9561305
## 48.28571 1.595752 -1.0579496
## 48.42857 1.504201 -1.1911704
## 48.57143 1.434067 -1.3029312
## 48.71429 1.354360 -1.4228583
## 48.85714 1.281737 -1.5352764
## 49.00000 1.206975 -1.6490441
## 
## $滋賀県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%      95%
## 47.14286 11.511841 13.59623
## 47.28571  9.819835 11.94043
## 47.42857 11.293304 13.63025
## 47.57143 10.864715 13.24783
## 47.71429 11.338562 13.80441
## 47.85714 11.273708 13.78510
## 48.00000 11.470118 14.03528
## 48.14286 11.508184 14.11682
## 48.28571 11.621674 14.27538
## 48.42857 11.687558 14.38293
## 48.57143 11.774691 14.51169
## 48.71429 11.846941 14.62416
## 48.85714 11.924668 14.74168
## 49.00000 11.997271 14.85329
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 21.01169 22.77068 23.84024 19.98613 17.44618 17.36747 20.31409 21.60480
##  [9] 20.96905 20.70986 18.67633 17.48054 18.87564 18.62939
## 
## $京都府$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 14.013425 10.308767
## 47.28571 15.099323 11.038352
## 47.42857 16.054530 11.933021
## 47.57143 12.142750  7.990716
## 47.71429  9.513499  5.314190
## 47.85714  9.291083  5.015701
## 48.00000 12.053947  7.681292
## 48.14286 12.949401  8.367508
## 48.28571 12.045561  7.321751
## 48.42857 11.575403  6.739916
## 48.57143  9.348455  4.410577
## 48.71429  7.960294  2.920580
## 48.85714  9.160424  4.017501
## 49.00000  8.718323  3.471724
## 
## $京都府$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 28.00996 31.71462
## 47.28571 30.44204 34.50301
## 47.42857 31.62596 35.74747
## 47.57143 27.82950 31.98154
## 47.71429 25.37886 29.57817
## 47.85714 25.44386 29.71924
## 48.00000 28.57423 32.94689
## 48.14286 30.26020 34.84209
## 48.28571 29.89254 34.61635
## 48.42857 29.84431 34.67980
## 48.57143 28.00420 32.94208
## 48.71429 27.00079 32.04050
## 48.85714 28.59085 33.73377
## 49.00000 28.54045 33.78705
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 420.6252 439.2444 485.8415 480.8537 360.5217 356.1114 436.5357 439.3610
##  [9] 464.4838 509.9718 485.2976 401.1901 401.8739 473.5450
## 
## $大阪府$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 387.3723 369.7693
## 47.28571 401.1608 381.0005
## 47.42857 446.1315 425.1104
## 47.57143 439.5815 417.7334
## 47.71429 317.7444 295.0994
## 47.85714 311.8800 288.4654
## 48.00000 390.8967 366.7368
## 48.14286 387.5370 360.1030
## 48.28571 409.3976 380.2368
## 48.42857 452.6789 422.3498
## 48.57143 425.8797 394.4258
## 48.71429 339.7208 307.1809
## 48.85714 338.4194 304.8287
## 49.00000 408.1656 373.5558
## 
## $大阪府$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 453.8780 471.4810
## 47.28571 477.3281 497.4884
## 47.42857 525.5514 546.5726
## 47.57143 522.1258 543.9740
## 47.71429 403.2991 425.9440
## 47.85714 400.3428 423.7575
## 48.00000 482.1748 506.3347
## 48.14286 491.1851 518.6190
## 48.28571 519.5700 548.7308
## 48.42857 567.2648 597.5938
## 48.57143 544.7154 576.1693
## 48.71429 462.6594 495.1993
## 48.85714 465.3284 498.9192
## 49.00000 538.9244 573.5342
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 163.63798 123.70692 148.19294 129.13930  89.03057 126.80304 130.90602
##  [8] 167.01222 133.39495 154.00931 137.96837 104.20153 136.00152 139.45575
## 
## $兵庫県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 150.48384 143.52045
## 47.28571 109.74174 102.34902
## 47.42857 133.46130 125.66283
## 47.57143 113.67914 105.49503
## 47.71429  72.87472  64.32234
## 47.85714 109.98024 101.07480
## 48.00000 113.44173 104.19670
## 48.14286 146.34409 135.40303
## 48.28571 111.58685 100.04233
## 48.42857 131.11793 118.99997
## 48.57143 114.04272 101.37724
## 48.71429  79.28449  66.09421
## 48.85714 110.13106  96.43607
## 49.00000 112.66579  98.48404
## 
## $兵庫県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 176.7921 183.7555
## 47.28571 137.6721 145.0648
## 47.42857 162.9246 170.7230
## 47.57143 144.5994 152.7836
## 47.71429 105.1864 113.7388
## 47.85714 143.6258 152.5313
## 48.00000 148.3703 157.6153
## 48.14286 187.6804 198.6214
## 48.28571 155.2031 166.7476
## 48.42857 176.9007 189.0187
## 48.57143 161.8940 174.5595
## 48.71429 129.1186 142.3088
## 48.85714 161.8720 175.5670
## 49.00000 166.2457 180.4275
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 24.04348 23.35005 22.66445 21.93865 22.92516 22.16572 22.76402 23.03589
##  [9] 23.44314 22.55290 22.75962 22.86475 22.63397 24.29323
## 
## $奈良県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 19.54338 17.16118
## 47.28571 18.68677 16.21818
## 47.42857 17.84352 15.29147
## 47.57143 16.96505 14.33219
## 47.71429 17.80344 15.09217
## 47.85714 16.90005 14.11258
## 48.00000 17.35823 14.49658
## 48.14286 17.41076 14.43300
## 48.28571 17.66675 14.60892
## 48.42857 16.62912 13.49326
## 48.57143 16.69202 13.48003
## 48.71429 16.65667 13.37031
## 48.85714 16.28852 12.92944
## 49.00000 17.81332 14.38305
## 
## $奈良県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 28.54357 30.92578
## 47.28571 28.01332 30.48191
## 47.42857 27.48539 30.03743
## 47.57143 26.91225 29.54511
## 47.71429 28.04687 30.75814
## 47.85714 27.43138 30.21886
## 48.00000 28.16981 31.03146
## 48.14286 28.66102 31.63879
## 48.28571 29.21952 32.27736
## 48.42857 28.47668 31.61254
## 48.57143 28.82722 32.03921
## 48.71429 29.07283 32.35919
## 48.85714 28.97943 32.33851
## 49.00000 30.77315 34.20341
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 7.871322 7.635691 8.472934 8.620724 8.339411 8.334656 8.641716 8.489135
##  [9] 7.696991 8.297551 8.139294 8.521031 8.145501 8.234063
## 
## $和歌山県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 5.726911 4.591728
## 47.28571 5.313603 4.084364
## 47.42857 6.056343 4.777077
## 47.57143 6.113189 4.785780
## 47.71429 5.744117 4.370251
## 47.85714 5.654475 4.235673
## 48.00000 5.879255 4.416896
## 48.14286 5.688757 4.206326
## 48.28571 4.836777 3.322671
## 48.42857 5.374324 3.826861
## 48.57143 5.154384 3.574268
## 48.71429 5.475686 3.863578
## 48.85714 5.040899 3.397422
## 49.00000 5.071313 3.397054
## 
## $和歌山県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%      95%
## 47.14286 10.015734 11.15092
## 47.28571  9.957779 11.18702
## 47.42857 10.889525 12.16879
## 47.57143 11.128259 12.45567
## 47.71429 10.934705 12.30857
## 47.85714 11.014837 12.43364
## 48.00000 11.404177 12.86654
## 48.14286 11.289513 12.77194
## 48.28571 10.557205 12.07131
## 48.42857 11.220778 12.76824
## 48.57143 11.124204 12.70432
## 48.71429 11.566375 13.17848
## 48.85714 11.250103 12.89358
## 49.00000 11.396813 13.07107
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 0.5240511 0.5909383 0.5867734 0.5252281 0.4540140 0.3669371 0.2647077
##  [8] 0.2825330 0.4542111 0.4638780 0.4654045 0.4609858 0.3674050 0.3561387
## 
## $鳥取県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                 80%        95%
## 47.14286 -0.4145685 -0.9114437
## 47.28571 -0.3612686 -0.8653364
## 47.42857 -0.3754196 -0.8847738
## 47.57143 -0.4512004 -0.9680904
## 47.71429 -0.5253595 -1.0438085
## 47.85714 -0.6144230 -1.1339237
## 48.00000 -0.7181584 -1.2384563
## 48.14286 -0.7003852 -1.2207107
## 48.28571 -0.5292072 -1.0497974
## 48.42857 -0.5199704 -1.0407884
## 48.57143 -0.5187791 -1.0397745
## 48.71429 -0.5235805 -1.0447784
## 48.85714 -0.6175341 -1.1389293
## 49.00000 -0.6291656 -1.1507541
## 
## $鳥取県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 1.462671 1.959546
## 47.28571 1.543145 2.047213
## 47.42857 1.548966 2.058321
## 47.57143 1.501657 2.018547
## 47.71429 1.433387 1.951836
## 47.85714 1.348297 1.867798
## 48.00000 1.247574 1.767872
## 48.14286 1.265451 1.785777
## 48.28571 1.437629 1.958220
## 48.42857 1.447727 1.968544
## 48.57143 1.449588 1.970584
## 48.71429 1.445552 1.966750
## 48.85714 1.352344 1.873739
## 49.00000 1.341443 1.863031
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 0.4582043 0.4582043 0.4582043 0.4582043 0.4582043 0.4582043 0.4582043
##  [8] 0.4582043 0.4582043 0.4582043 0.4582043 0.4582043 0.4582043 0.4582043
## 
## $島根県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 -6.133257 -9.622566
## 47.28571 -6.133257 -9.622566
## 47.42857 -6.133257 -9.622566
## 47.57143 -6.133257 -9.622566
## 47.71429 -6.133257 -9.622566
## 47.85714 -6.133257 -9.622566
## 48.00000 -6.133257 -9.622566
## 48.14286 -6.133257 -9.622566
## 48.28571 -6.133257 -9.622566
## 48.42857 -6.133257 -9.622566
## 48.57143 -6.133257 -9.622566
## 48.71429 -6.133257 -9.622566
## 48.85714 -6.133257 -9.622566
## 49.00000 -6.133257 -9.622566
## 
## $島根県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 7.049666 10.53897
## 47.28571 7.049666 10.53897
## 47.42857 7.049666 10.53897
## 47.57143 7.049666 10.53897
## 47.71429 7.049666 10.53897
## 47.85714 7.049666 10.53897
## 48.00000 7.049666 10.53897
## 48.14286 7.049666 10.53897
## 48.28571 7.049666 10.53897
## 48.42857 7.049666 10.53897
## 48.57143 7.049666 10.53897
## 48.71429 7.049666 10.53897
## 48.85714 7.049666 10.53897
## 49.00000 7.049666 10.53897
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 11.50615 13.64988 11.80026 11.23336 10.91976 11.53966 11.38645 11.62762
##  [9] 11.68826 11.70350 11.70734 11.70830 11.70854 11.70860
## 
## $岡山県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%      95%
## 47.14286  8.187776 6.431138
## 47.28571 10.078402 8.187775
## 47.42857  8.136243 6.196627
## 47.57143  7.504571 5.530667
## 47.71429  7.132963 5.128355
## 47.85714  7.697098 5.662969
## 48.00000  7.489264 5.426216
## 48.14286  7.545826 5.385053
## 48.28571  7.513931 5.304176
## 48.42857  7.455331 5.206484
## 48.57143  7.390447 5.105223
## 48.71429  7.324712 5.004181
## 48.85714  7.259488 4.904300
## 49.00000  7.195089 4.805779
## 
## $岡山県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 14.82451 16.58115
## 47.28571 17.22136 19.11199
## 47.42857 15.46428 17.40390
## 47.57143 14.96216 16.93606
## 47.71429 14.70655 16.71116
## 47.85714 15.38222 17.41635
## 48.00000 15.28364 17.34669
## 48.14286 15.70942 17.87020
## 48.28571 15.86259 18.07234
## 48.42857 15.95168 18.20052
## 48.57143 16.02423 18.30945
## 48.71429 16.09189 18.41242
## 48.85714 16.15760 18.51279
## 49.00000 16.22212 18.61143
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 16.05933 16.73772 17.97967 16.79693 18.43010 17.85747 16.15543 18.34700
##  [9] 18.39487 19.63674 19.98170 17.99696 18.71773 19.56694
## 
## $広島県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 12.20815 10.169453
## 47.28571 12.44262 10.168933
## 47.42857 13.52123 11.161074
## 47.57143 11.72881  9.045912
## 47.71429 12.94198 10.036751
## 47.85714 12.10435  9.058827
## 48.00000 10.04732  6.813889
## 48.14286 12.02948  8.685194
## 48.28571 11.85471  8.392550
## 48.42857 12.84132  9.244052
## 48.57143 12.96883  9.256444
## 48.71429 10.77568  6.952972
## 48.85714 11.28105  7.344303
## 49.00000 11.92623  7.881476
## 
## $広島県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 19.91051 21.94921
## 47.28571 21.03281 23.30650
## 47.42857 22.43812 24.79828
## 47.57143 21.86505 24.54796
## 47.71429 23.91821 26.82344
## 47.85714 23.61059 26.65611
## 48.00000 22.26353 25.49696
## 48.14286 24.66452 28.00881
## 48.28571 24.93504 28.39720
## 48.42857 26.43215 30.02942
## 48.57143 26.99457 30.70696
## 48.71429 25.21823 29.04095
## 48.85714 26.15442 30.09117
## 49.00000 27.20766 31.25241
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 6.451419 6.598482 6.485680 4.567226 3.993155 3.499467 4.748693 3.815222
##  [9] 3.491776 4.233786 3.509272 3.556805 3.869170 4.073810
## 
## $山口県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                  80%        95%
## 47.14286  3.79335949  2.3862678
## 47.28571  3.70751358  2.1771272
## 47.42857  3.43344355  1.8176869
## 47.57143  1.39968546 -0.2771093
## 47.71429  0.74118161 -0.9803090
## 47.85714  0.18442003 -1.5704596
## 48.00000  1.38566882 -0.3946084
## 48.14286  0.33057041 -1.5140930
## 48.28571 -0.04808522 -1.9219745
## 48.42857  0.65102142 -1.2455798
## 48.57143 -0.10742207 -2.0219843
## 48.71429 -0.08718853 -2.0162021
## 48.85714  0.20283282 -1.7380091
## 49.00000  0.38887082 -1.5618182
## 
## $山口県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 9.109479 10.516570
## 47.28571 9.489451 11.019837
## 47.42857 9.537917 11.153674
## 47.57143 7.734767  9.411562
## 47.71429 7.245128  8.966618
## 47.85714 6.814513  8.569393
## 48.00000 8.111716  9.891993
## 48.14286 7.299875  9.144538
## 48.28571 7.031637  8.905526
## 48.42857 7.816551  9.713153
## 48.57143 7.125966  9.040529
## 48.71429 7.200798  9.129812
## 48.85714 7.535508  9.476350
## 49.00000 7.758749  9.709438
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 0.30254282 0.21690110 0.05805455 0.13930907 0.28758433 0.38743815
##  [7] 0.39246326 0.26804108 0.29898762 0.40588536 0.41571667 0.39537232
## [13] 0.34836807 0.35906694
## 
## $徳島県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 -1.389290 -2.284892
## 47.28571 -1.557793 -2.497259
## 47.42857 -1.734905 -2.684041
## 47.57143 -1.671733 -2.630440
## 47.71429 -1.541361 -2.509546
## 47.85714 -1.459236 -2.436807
## 48.00000 -1.471772 -2.458639
## 48.14286 -1.687514 -2.722722
## 48.28571 -1.689701 -2.742449
## 48.42857 -1.606189 -2.671316
## 48.57143 -1.619474 -2.696839
## 48.71429 -1.662676 -2.752141
## 48.85714 -1.732286 -2.833718
## 49.00000 -1.743950 -2.857221
## 
## $徳島県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 1.994375 2.889977
## 47.28571 1.991595 2.931061
## 47.42857 1.851015 2.800150
## 47.57143 1.950351 2.909059
## 47.71429 2.116529 3.084714
## 47.85714 2.234113 3.211683
## 48.00000 2.256699 3.243565
## 48.14286 2.223596 3.258804
## 48.28571 2.287676 3.340424
## 48.42857 2.417959 3.483087
## 48.57143 2.450908 3.528273
## 48.71429 2.453420 3.542885
## 48.85714 2.429022 3.530454
## 49.00000 2.462084 3.575355
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 1.335711 1.589987 1.589987 1.589987 1.589987 1.589987 1.589987 1.589987
##  [9] 1.589987 1.589987 1.589987 1.589987 1.589987 1.589987
## 
## $香川県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                   80%        95%
## 47.14286 -0.124084705 -0.8968537
## 47.28571  0.076989390 -0.7239432
## 47.42857  0.068971003 -0.7362063
## 47.57143  0.060994665 -0.7484050
## 47.71429  0.053059723 -0.7605405
## 47.85714  0.045165538 -0.7726136
## 48.00000  0.037311488 -0.7846253
## 48.14286  0.029496967 -0.7965766
## 48.28571  0.021721385 -0.8084683
## 48.42857  0.013984166 -0.8203014
## 48.57143  0.006284746 -0.8320766
## 48.71429 -0.001377423 -0.8437949
## 48.85714 -0.009002875 -0.8554570
## 49.00000 -0.016592135 -0.8670638
## 
## $香川県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 2.795506 3.568275
## 47.28571 3.102985 3.903917
## 47.42857 3.111003 3.916180
## 47.57143 3.118980 3.928379
## 47.71429 3.126914 3.940515
## 47.85714 3.134809 3.952588
## 48.00000 3.142663 3.964600
## 48.14286 3.150477 3.976551
## 48.28571 3.158253 3.988443
## 48.42857 3.165990 4.000276
## 48.57143 3.173689 4.012051
## 48.71429 3.181352 4.023769
## 48.85714 3.188977 4.035431
## 49.00000 3.196566 4.047038
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 6.017021 6.822706 7.483895 7.507924 8.129223 7.844387 7.771112 7.798811
##  [9] 7.798811 7.798811 7.798811 7.798811 7.798811 7.798811
## 
## $愛媛県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%         95%
## 47.14286 3.702478  2.47723388
## 47.28571 4.092211  2.64677489
## 47.42857 4.392925  2.75666440
## 47.57143 4.094335  2.28729038
## 47.71429 4.420977  2.45795030
## 47.85714 3.863234  1.75573856
## 48.00000 3.534595  1.29191799
## 48.14286 3.433436  1.12254581
## 48.28571 3.271267  0.87453047
## 48.42857 3.114710  0.63509654
## 48.57143 2.963219  0.40341048
## 48.71429 2.816331  0.17876554
## 48.85714 2.673652 -0.03944371
## 49.00000 2.534838 -0.25174059
## 
## $愛媛県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286  8.331563  9.556807
## 47.28571  9.553200 10.998637
## 47.42857 10.574865 12.211126
## 47.57143 10.921514 12.728558
## 47.71429 11.837468 13.800495
## 47.85714 11.825540 13.933035
## 48.00000 12.007629 14.250305
## 48.14286 12.164187 14.475077
## 48.28571 12.326355 14.723092
## 48.42857 12.482913 14.962526
## 48.57143 12.634404 15.194212
## 48.71429 12.781291 15.418857
## 48.85714 12.923971 15.637066
## 49.00000 13.062784 15.849363
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 2.773120 2.579618 2.403145 2.242204 2.095427 1.961567 1.839489 1.728154
##  [9] 1.626618 1.534018 1.449568 1.372550 1.302311 1.238253
## 
## $高知県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                  80%         95%
## 47.14286  1.01710926  0.08753311
## 47.28571  0.79005174 -0.15728726
## 47.42857  0.58614259 -0.37572041
## 47.57143  0.40269319 -0.57108483
## 47.71429  0.23740329 -0.74617482
## 47.85714  0.08828569 -0.90336964
## 48.00000 -0.04638945 -1.04471306
## 48.14286 -0.16813714 -1.17197319
## 48.28571 -0.27829074 -1.28668868
## 48.42857 -0.37802854 -1.39020506
## 48.57143 -0.46839533 -1.48370390
## 48.71429 -0.55032026 -1.56822649
## 48.85714 -0.62463165 -1.64469339
## 49.00000 -0.69206959 -1.71392066
## 
## $高知県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 4.529132 5.458708
## 47.28571 4.369184 5.316523
## 47.42857 4.220148 5.182011
## 47.57143 4.081714 5.055492
## 47.71429 3.953450 4.937028
## 47.85714 3.834849 4.826504
## 48.00000 3.725367 4.723691
## 48.14286 3.624446 4.628282
## 48.28571 3.531527 4.539925
## 48.42857 3.446065 4.458242
## 48.57143 3.367532 4.382840
## 48.71429 3.295421 4.313327
## 48.85714 3.229253 4.249315
## 49.00000 3.168576 4.190427
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 55.40546 60.26822 56.69956 46.71245 37.14558 38.07901 45.10142 54.89344
##  [9] 58.53616 56.03199 48.99920 42.87442 42.68658 46.61285
## 
## $福岡県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%        95%
## 47.14286 41.93128 34.7984745
## 47.28571 44.28904 35.8301747
## 47.42857 39.41739 30.2687583
## 47.57143 28.77831 19.2845438
## 47.71429 18.67649  8.8995517
## 47.85714 18.79378  8.5847930
## 48.00000 24.49336 13.5841081
## 48.14286 31.62899 19.3135397
## 48.28571 33.34919 20.0160050
## 48.42857 29.59245 15.5961985
## 48.57143 21.78857  7.3841264
## 48.71429 15.02725  0.2858467
## 48.85714 14.03653 -1.1298904
## 49.00000 16.81888  1.0468999
## 
## $福岡県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 68.87964 76.01245
## 47.28571 76.24739 84.70626
## 47.42857 73.98173 83.13036
## 47.57143 64.64660 74.14036
## 47.71429 55.61466 65.39160
## 47.85714 57.36424 67.57322
## 48.00000 65.70949 76.61874
## 48.14286 78.15788 90.47334
## 48.28571 83.72314 97.05632
## 48.42857 82.47153 96.46778
## 48.57143 76.20983 90.61427
## 48.71429 70.72159 85.46299
## 48.85714 71.33664 86.50306
## 49.00000 76.40682 92.17879
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 4.629051 4.629051 4.629051 4.629051 4.629051 4.629051 4.629051 4.629051
##  [9] 4.629051 4.629051 4.629051 4.629051 4.629051 4.629051
## 
## $佐賀県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%        95%
## 47.14286 2.482141 1.34563496
## 47.28571 2.408711 1.23333427
## 47.42857 2.337633 1.12463045
## 47.57143 2.268695 1.01919856
## 47.71429 2.201714 0.91675981
## 47.85714 2.136532 0.81707289
## 48.00000 2.073012 0.71992726
## 48.14286 2.011033 0.62513796
## 48.28571 1.950487 0.53254140
## 48.42857 1.891280 0.44199212
## 48.57143 1.833327 0.35336002
## 48.71429 1.776551 0.26652827
## 48.85714 1.720883 0.18139141
## 49.00000 1.666260 0.09785391
## 
## $佐賀県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 6.775961 7.912466
## 47.28571 6.849390 8.024767
## 47.42857 6.920468 8.133471
## 47.57143 6.989406 8.238903
## 47.71429 7.056387 8.341342
## 47.85714 7.121569 8.441028
## 48.00000 7.185089 8.538174
## 48.14286 7.247068 8.632963
## 48.28571 7.307614 8.725560
## 48.42857 7.366821 8.816109
## 48.57143 7.424774 8.904741
## 48.71429 7.481551 8.991573
## 48.85714 7.537219 9.076710
## 49.00000 7.591841 9.160247
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 1.0395933 1.1936850 0.7934668 0.7792827 0.5259998 0.8163212 0.4644793
##  [8] 0.6536668 0.8984704 0.4229860 0.6941918 0.4593215 0.7007076 0.4434290
## 
## $長崎県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 -1.064823 -2.178834
## 47.28571 -1.138832 -2.373592
## 47.42857 -2.023430 -3.514605
## 47.57143 -2.314829 -3.952753
## 47.71429 -2.885219 -4.691009
## 47.85714 -2.854070 -4.797057
## 48.00000 -3.462889 -5.541913
## 48.14286 -3.653111 -5.932981
## 48.28571 -3.687017 -6.114428
## 48.42857 -4.458898 -7.043211
## 48.57143 -4.450251 -7.173554
## 48.71429 -4.943243 -7.803188
## 48.85714 -4.944024 -7.932165
## 49.00000 -5.435587 -8.547751
## 
## $長崎県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 3.144010 4.258020
## 47.28571 3.526202 4.760962
## 47.42857 3.610364 5.101539
## 47.57143 3.873395 5.511319
## 47.71429 3.937219 5.743009
## 47.85714 4.486712 6.429700
## 48.00000 4.391848 6.470871
## 48.14286 4.960444 7.240314
## 48.28571 5.483958 7.911369
## 48.42857 5.304870 7.889183
## 48.57143 5.838634 8.561938
## 48.71429 5.861886 8.721831
## 48.85714 6.345439 9.333580
## 49.00000 6.322446 9.434609
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1]  4.423080 10.300225  9.165965  9.165965  9.165965  9.165965  9.165965
##  [8]  9.165965  9.165965  9.165965  9.165965  9.165965  9.165965  9.165965
## 
## $熊本県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                 80%        95%
## 47.14286 -0.0696194 -2.4479109
## 47.28571  4.5755552  1.5450983
## 47.42857  3.3173579  0.2212921
## 47.57143  3.0024881 -0.2602597
## 47.71429  2.7029401 -0.7183787
## 47.85714  2.4166737 -1.1561854
## 48.00000  2.1420647 -1.5761637
## 48.14286  1.8777953 -1.9803289
## 48.28571  1.6227786 -2.3703432
## 48.42857  1.3761060 -2.7475964
## 48.57143  1.1370083 -3.1132648
## 48.71429  0.9048278 -3.4683542
## 48.85714  0.6789967 -3.8137331
## 49.00000  0.4590210 -4.1501568
## 
## $熊本県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286  8.91578 11.29407
## 47.28571 16.02489 19.05535
## 47.42857 15.01457 18.11064
## 47.57143 15.32944 18.59219
## 47.71429 15.62899 19.05031
## 47.85714 15.91526 19.48812
## 48.00000 16.18987 19.90809
## 48.14286 16.45414 20.31226
## 48.28571 16.70915 20.70227
## 48.42857 16.95582 21.07953
## 48.57143 17.19492 21.44520
## 48.71429 17.42710 21.80028
## 48.85714 17.65293 22.14566
## 49.00000 17.87291 22.48209
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 13.49891 13.86167 11.50365 13.46055 11.99442 12.30692 12.59819 12.48641
##  [9] 12.64071 13.49240 12.43559 13.18832 13.02570 13.20201
## 
## $大分県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                80%       95%
## 47.14286 11.484864 10.418690
## 47.28571 11.648738 10.477284
## 47.42857  9.017290  7.701090
## 47.57143 10.810643  9.407865
## 47.71429  9.191270  7.707371
## 47.85714  9.374979  7.822901
## 48.00000  9.547004  7.931805
## 48.14286  9.376076  7.729564
## 48.28571  9.448748  7.759028
## 48.42857 10.227412  8.499033
## 48.57143  9.094927  7.326486
## 48.71429  9.774138  7.966778
## 48.85714  9.538639  7.692700
## 49.00000  9.643452  7.759664
## 
## $大分県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 15.51296 16.57914
## 47.28571 16.07460 17.24606
## 47.42857 13.99001 15.30621
## 47.57143 16.11047 17.51324
## 47.71429 14.79757 16.28147
## 47.85714 15.23887 16.79095
## 48.00000 15.64937 17.26457
## 48.14286 15.59675 17.24326
## 48.28571 15.83266 17.52238
## 48.42857 16.75738 18.48576
## 48.57143 15.77625 17.54469
## 48.71429 16.60251 18.40987
## 48.85714 16.51276 18.35870
## 49.00000 16.76058 18.64436
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 9.493570 9.144977 9.144977 9.144977 9.144977 9.144977 9.144977 9.144977
##  [9] 9.144977 9.144977 9.144977 9.144977 9.144977 9.144977
## 
## $宮崎県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%         95%
## 47.14286 6.468443  4.86703817
## 47.28571 5.969391  4.28833782
## 47.42857 5.645083  3.79235132
## 47.57143 5.348377  3.33857887
## 47.71429 5.073235  2.91778528
## 47.85714 4.815544  2.52367997
## 48.00000 4.572351  2.15174943
## 48.14286 4.341456  1.79862488
## 48.28571 4.121161  1.46171291
## 48.42857 3.910128  1.13896647
## 48.57143 3.707279  0.82873613
## 48.71429 3.511730  0.52966970
## 48.85714 3.322746  0.24064225
## 49.00000 3.139705 -0.03929406
## 
## $宮崎県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 12.51870 14.12010
## 47.28571 12.32056 14.00162
## 47.42857 12.64487 14.49760
## 47.57143 12.94158 14.95138
## 47.71429 13.21672 15.37217
## 47.85714 13.47441 15.76627
## 48.00000 13.71760 16.13820
## 48.14286 13.94850 16.49133
## 48.28571 14.16879 16.82824
## 48.42857 14.37983 17.15099
## 48.57143 14.58267 17.46122
## 48.71429 14.77822 17.76028
## 48.85714 14.96721 18.04931
## 49.00000 15.15025 18.32925
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 4.568317 4.626165 4.360837 4.224443 4.224443 4.224443 4.224443 4.224443
##  [9] 4.224443 4.224443 4.224443 4.224443 4.224443 4.224443
## 
## $鹿児島県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##                 80%       95%
## 47.14286  0.4814061 -1.682074
## 47.28571 -0.1558480 -2.687293
## 47.42857 -0.6470129 -3.298009
## 47.57143 -0.8673569 -3.562793
## 47.71429 -0.8848538 -3.589552
## 47.85714 -0.9022909 -3.616220
## 48.00000 -0.9196689 -3.642797
## 48.14286 -0.9369884 -3.669285
## 48.28571 -0.9542500 -3.695684
## 48.42857 -0.9714543 -3.721996
## 48.57143 -0.9886017 -3.748221
## 48.71429 -1.0056930 -3.774360
## 48.85714 -1.0227286 -3.800413
## 49.00000 -1.0397090 -3.826383
## 
## $鹿児島県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 8.655228 10.81871
## 47.28571 9.408179 11.93962
## 47.42857 9.368688 12.01968
## 47.57143 9.316242 12.01168
## 47.71429 9.333739 12.03844
## 47.85714 9.351176 12.06511
## 48.00000 9.368554 12.09168
## 48.14286 9.385874 12.11817
## 48.28571 9.403135 12.14457
## 48.42857 9.420340 12.17088
## 48.57143 9.437487 12.19711
## 48.71429 9.454578 12.22325
## 48.85714 9.471614 12.24930
## 49.00000 9.488594 12.27527
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##  [1] 54.31220 45.26875 56.71634 48.68100 45.06466 43.37835 48.92012 47.19100
##  [9] 47.19100 47.19100 47.19100 47.19100 47.19100 47.19100
## 
## $沖縄県$lower
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%       95%
## 47.14286 41.70623 35.033025
## 47.28571 31.67009 24.471397
## 47.42857 42.19269 34.504341
## 47.57143 33.28785 25.139209
## 47.71429 28.84857 20.264283
## 47.85714 26.37910 17.380237
## 48.00000 31.17224 21.777075
## 48.14286 27.28622 16.749274
## 48.28571 26.19432 15.079351
## 48.42857 25.15646 13.492079
## 48.57143 24.16533 11.976279
## 48.71429 23.21514 10.523087
## 48.85714 22.30120  9.125332
## 49.00000 21.41964  7.777114
## 
## $沖縄県$upper
## Time Series:
## Start = c(47, 2) 
## End = c(49, 1) 
## Frequency = 7 
##               80%      95%
## 47.14286 66.91818 73.59138
## 47.28571 58.86740 66.06609
## 47.42857 71.23998 78.92833
## 47.57143 64.07414 72.22278
## 47.71429 61.28076 69.86504
## 47.85714 60.37760 69.37646
## 48.00000 66.66800 76.06316
## 48.14286 67.09577 77.63272
## 48.28571 68.18767 79.30264
## 48.42857 69.22553 80.88991
## 48.57143 70.21666 82.40571
## 48.71429 71.16685 83.85891
## 48.85714 72.08080 85.25666
## 49.00000 72.96235 86.60488